






'''simple docstring'''




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class   _UpperCAmelCase     (	snake_case_		):




   """simple docstring"""

   snake_case       							=		"""EncodecFeatureExtractor"""
   snake_case       							=		("""T5Tokenizer""", """T5TokenizerFast""")

   def __init__(						self					:      List[Any] , __UpperCAmelCase					:      Optional[int] , __UpperCAmelCase					:      List[str]						):





         '''simple docstring'''





         super().__init__(__UpperCAmelCase , __UpperCAmelCase						)
         _A     	=      self.feature_extractor
         _A     	=      False

   def 		lowerCAmelCase					(						self					:      List[str] , __UpperCAmelCase					:      List[str]=None , __UpperCAmelCase					:      Tuple=None , __UpperCAmelCase					:      int=True						):





         '''simple docstring'''





         return self.tokenizer.get_decoder_prompt_ids(task=__UpperCAmelCase , language=__UpperCAmelCase , no_timestamps=__UpperCAmelCase						)

   def __call__(						self					:      Any , *__UpperCAmelCase					:      int , **__UpperCAmelCase					:      List[Any]						):





         '''simple docstring'''





         if self._in_target_context_manager:
               return self.current_processor(*__UpperCAmelCase , **__UpperCAmelCase						)

         _A     	=      kwargs.pop("audio" , __UpperCAmelCase						)
         _A     	=      kwargs.pop("sampling_rate" , __UpperCAmelCase						)
         _A     	=      kwargs.pop("text" , __UpperCAmelCase						)
         if len(__UpperCAmelCase						) > 0:
               _A     	=      args[0]
               _A     	=      args[1:]

         if audio is None and text is None:
               raise ValueError("You need to specify either an `audio` or `text` input to process."						)

         if text is not None:
               _A     	=      self.tokenizer(__UpperCAmelCase , **__UpperCAmelCase						)

         if audio is not None:
               _A     	=      self.feature_extractor(__UpperCAmelCase , *__UpperCAmelCase , sampling_rate=__UpperCAmelCase , **__UpperCAmelCase						)

         if audio is None:
               return inputs

         elif text is None:
               return audio_inputs

         else:
               _A     	=      audio_inputs["input_values"]
               if "padding_mask" in audio_inputs:
                     _A     	=      audio_inputs["padding_mask"]
               return inputs

   def 		lowerCAmelCase					(						self					:      Dict , *__UpperCAmelCase					:      List[str] , **__UpperCAmelCase					:      Tuple						):





         '''simple docstring'''





         _A     	=      kwargs.pop("audio" , __UpperCAmelCase						)
         _A     	=      kwargs.pop("padding_mask" , __UpperCAmelCase						)

         if len(__UpperCAmelCase						) > 0:
               _A     	=      args[0]
               _A     	=      args[1:]

         if audio_values is not None:
               return self._decode_audio(__UpperCAmelCase , padding_mask=__UpperCAmelCase						)
         else:
               return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase						)

   def 		lowerCAmelCase					(						self					:      Optional[Any] , *__UpperCAmelCase					:      List[str] , **__UpperCAmelCase					:      Any						):





         '''simple docstring'''





         return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase						)

   def 		lowerCAmelCase					(						self					:      List[Any] , __UpperCAmelCase					:      Any , __UpperCAmelCase					:      Optional[Any] = None						):





         '''simple docstring'''





         _A     	=      to_numpy(__UpperCAmelCase						)
         _A	,      _A	,      _A     	=      audio_values.shape

         if padding_mask is None:
               return list(__UpperCAmelCase						)

         _A     	=      to_numpy(__UpperCAmelCase						)

         # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
         # token (so that the generated audio values are **not** treated as padded tokens)
         _A     	=      seq_len - padding_mask.shape[-1]
         _A     	=      1 - self.feature_extractor.padding_value
         _A     	=      np.pad(__UpperCAmelCase , ((0, 0), (0, difference)) , "constant" , constant_values=__UpperCAmelCase						)

         _A     	=      audio_values.tolist()
         for i in range(__UpperCAmelCase						):
               _A     	=      np.asarray(audio_values[i]						)[
                   padding_mask[i][None, :] != self.feature_extractor.padding_value
               ]
               _A     	=      sliced_audio.reshape(__UpperCAmelCase , -1						)

         return audio_values






"""simple docstring"""



from typing import Union

import fire
import torch
from tqdm import tqdm



def 	_lowerCamelCase(	a   ,    a = "cpu"   ,    a = None				):
       __a       =    torch.load(a   ,    map_location=a				)
       for k, v in tqdm(state_dict.items()				):
              if not isinstance(a   ,    torch.Tensor				):
                     raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"				)
              __a       =    v.half()
       if save_path is None:  # overwrite src_path
              __a       =    src_path
       torch.save(a   ,    a				)


if __name__ == "__main__":
   fire.Fire(convert)
